Rancang Bangun Sistem Monitoring EMI Berbasis Internet of Things

Abstract

In the continuously evolving digital era, the use of electronic devices and wireless communication systems is increasingly widespread across various sectors of life, from industry and transportation to households. Along with that, exposure to electromagnetic fields has also increased significantly. Excessive or uncontrolled electromagnetic fields can have negative impacts, both on the performance of electronic devices and on human health, especially when exposed to high levels continuously in the environment. However, currently, monitoring the intensity of electromagnetic fields in the surrounding environment is still limited. Most existing detection systems are manual, expensive, and do not provide real-time data. Therefore, a monitoring solution based on Internet of Things (IoT) technology is needed, capable of continuously detecting EMF exposure, providing early warnings, and reducing potential interference with electronic systems and user safety. Based on this background, this research aims to design and build an IoT-based EMI monitoring system capable of continuously monitoring electromagnetic field conditions online and in real-time. Additionally, this research also aims to analyze the comparison of EMI values measured using an electromagnetic wave detection sensor and an electromagnetic radiation tester, taking into account the variations in distance and load used in the testing. The results of the design of the internet of things-based magnetic field intensity measuring instrument can be displayed through a smartphone, where the parameters are shown. This monitoring system allows remote monitoring through an internet of things-based application, namely the configured Blynk software. The analysis results of the magnetic field intensity measuring instrument designed with standard measuring instruments at various load variations show that if an electrical source receives a large electric current, the magnetic field intensity also increases. In the distance variation with a 2A load, the highest error percentage was obtained, which was 83.18%. Meanwhile, the lowest error percentage of 65.55% was obtained in the distance variation with a 16A load.